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Evers, S. and Fokkinga, M.M. and Apers, P.M.G.
(2008)
Probabilistic Processing of Interval-valued Sensor Data.
In: Proceedings of the 5th International Workshop on Data Management for Sensor Networks (DMSN2008), 24 Aug 2008, Auckland, New Zealand.
pp. 42-48.
ACM International Conference Proceeding Series.
ACM.
ISBN 978-1-60558-284-9
Full text available as:
Official URL: http://doi.acm.org/10.1145/1402050.1402060  AbstractWhen dealing with sensors with different time resolutions, it is desirable to model a sensor reading as pertaining to a time interval rather than a unit of time. We introduce two variants on the Hidden Markov Model in which this is possible: a reading extends over an arbitrary number of hidden states. We derive inference algorithms for the models, and analyse their efficiency. For this, we introduce a new method: we start with an inefficient algorithm directly derived from the model, and visually optimize it using a sum-factor diagram.
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